CC BY
0Hindawi Publishing Corporation Discrete Dynamics in Nature and Society Volume 2012, Article ID 260962,13 pages doi:10.1155/2012/260962
Research Article
An Impulse Model for Computer Viruses
Chunming Zhang, Yun Zhao, and Yingjiang Wu
School of Information Engineering, Guangdong Medical College, Dongguan 523808, China Correspondence should be addressed to Yun Zhao, zyun@gdmc.edu.cn Received 9 May 2012; Accepted 4 June 2012 Academic Editor: Xiaofan Yang
Copyright © 2012 Chunming Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Computer virus spread model concerning impulsive control strategy is proposed and analyzed. We prove that there exists a globally attractive infection-free periodic solution when the vaccination rate is larger than 0O. Moreover, we show that the system is uniformly persistent if the vaccination rate is less than 01. Some numerical simulations are finally given to illustrate the main results.
1. Introduction
Computer virus is a kind of computer program that can replicate itself and spread from one computer to others. Viruses mainly attack the file system and worms use system vulnerability to search and attack computers. As hardware and software technology develop and computer networks become an essential tool for daily life, the computer virus starts to be a major threat. Consequently, the trial on better understanding of the computer virus propagation dynamics is an important matter for improving the safety and reliability in computer systems and networks. Similar to the biological virus, there are two ways to study this problem: microscopic and macroscopic models. Following a macroscopic approach, since [1, 2] took the first step towards modeling the spread behavior of computer virus, much effort has been done in the area of developing a mathematical model for the computer virus propagation [313]. These models provide a reasonable qualitative understanding of the conditions under which viruses spread much faster than others.
In [4], the authors investigated a differential SIRS model by making the following assumptions.
(H1) The total population of computers is divided into three groups: susceptible, infected, and recovered computers. Let S,I, and R denote the numbers of susceptible, infected and recovered computers, respectively.
vR(t - T2)
(1 - p)b ^ > ßS(t - Ti)I(t - Ti) ^ I r1 ^ \ / R
Figure 1: Original model.
(H2) New computers are attached to the computer network with constant rate b. For the sake of antivirus software, some new nodes have temporary immunity with probability p, some have not with probability 1 - p. Hence, new nodes are added into the susceptible class with rate (1 -p)b and into the recovered class with rate pb.
(H3) Computers are disconnected to the computer network with the constant rate ¡i and are disconnected from the attack of malicious object with probability a.
(H4) S computers become I with constant rate ¡5 and with constant time delay t1; R computers become S with constant rate j and with constant time delay t1; I computers become R with constant rate j.
According to the above assumptions, the following model (see Figure 1) is derived :
= (1 - p)b - /S - ßS(t - ti)I(t - ti) + vR(t - T2), d = ßS(t - ti)I(t - ti) - (/ + j + a)I, § = pb - jI - /R - vR(t - t2) .
As we know, antivirus software is a kind of computer program which can detect and eliminate known viruses. There are two common methods that an antivirus software application uses to detect viruses: using a list of virus signature definitions and using a heuristic algorithm to find viruses based on common behaviors. It has been observed that it does not always work in detecting a novel computer virus by using the heuristic algorithm. On the other hand, obviously, it is impossible for antivirus software to find new computer viruss signature definitions on the dated list. So, to keep the antivirus soft in high efficiency, it is important to ensure that it is updated. Based on the above facts, we propose an impulsive system to model the process of periodic installing or updating antivirus software on susceptible computers at fixed time for controlling the spread of computer virus.
Based on above facts, we propose the following assumptions.
(H5) The antivirus software is installed or updated at time t = kT (k e N), where T is the period of the impulsive effect.
(H6) S computers are successfully vaccinated from S class to R class with rate d (0 < 0 < 1).
According to the above assumptions (H1)-(H6), and for the reason of simplicity we propose the following model with one time delay (see Figure 2):
— = (1 - p)b - pS - pSI(t - t)+ vR,
—t = pSI (t - t) - (p + y + a) I, tf kT, k e Z+,
—R = pb + yI - pR - vR, (1.2)
S(t+) = (1 - d)S(t), I (t+) = I (t), t = kT, R(t+) = R(t)+ 0S(t).
The total population size N(t) can be determined by N(t) = S(t) + I(t) + R(t) to form the differential equation
N(t) = b - ¡iN(t) - aI(t), (1.3)
which is derived by adding the equations in system (1.1). Thus the total population size N may vary in time. From (1.2), we have
b - (i + a)N(t) < Ni (t) < b - ¡1N(t). (1.4)
It follows that b/(i + a) < inf limx^xN(t) < limxsup N(t) < b/¡.
Before going into any details, we simplify model (1.1) and restrict our attention to the following model:
(1 - p)b + pSI(t - t)+ v(N - S -1), j5SI(t - t) - (i + y + a)I, t/kT, k e Z+,
b - iN - aI, (1.5)
(1 - O)S(t), I (t), t = kT, N(t).
dS = dt = d! = dt = dN = dt =
S(t+) =
I (t+) =
N (t+) =
The initial conditions for (1.5) are
(01(£),fc(£),fc(£)) e C+ = C([-w,0],R3+), (fcti)) > 0, i = 1,2,3.
(1 - p)b
fiSI(t - T) I rI > Sf
Figure 2: Impulse Model.
From physical considerations, we discuss system (1.5) in the closed set
Q = J (SJrN) G R+ | 0 < S +I < -, 0 < N <-
where R+ denotes the nonnegative cone of R3 including its lower-dimensional faces. Note that it is positively invariant with respect to (1.7). The organization of this paper is as follows. In Section 2, we first state three lemmas which are essential to our proofs and establish sufficient condition for the global attractivity of infection-free periodic solution. The sufficient condition for the permanence of the model is obtained in Section 3. Some numerical simulations are performed in Section 4. In the final section, a brief conclusion is given and some future research directions are also pointed out.
2. Global Attractivity of Infection-Free Periodic Solution
In this section, we prove that the infection-free periodic solution is globally attractive under some conditions. To prove the main results, two lemmas (given in [14]) which are essential to the proofs are stated here.
Lemma 2.1 (see [14], Lemma 1). Consider the following impulsive system:
u (t) = a - bu(t), t / kT, u(t+) = (1 - d)u(t), t = kT,
where a> 0, b> 0,0 <9 < 1. Then there exists a unique positive periodic solution of system (2.1)
,(t) = a + (u* - a)e-b(t-kT), kT<t < (k + 1)t,
which is globally asymptotically stable, where u* = a(1 - 0)(1 - e bT)/(b(1 - (1 - 9)e bT)).
Lemma 2.2 (see [14], Lemma 2). Consider the following linear neutral delay equation:
x(t) + Xx(t - w) + a2x(t) - a1x(t - w) = 0. (2.3)
If |X| < 1,a'1 < a2 or -a1 = a2 = 0, then increasing t does not change the stability of (2.3). When X = 0, (2.3) becomes
x(t) = a1x(t - w) - a2x(t). (2.4)
Corollary 2.3. Consider system (2.4) and assume that a1, a2, w > 0; x(t) > 0 for -w < t < 0. Then we have the following statements:
(i) assume that a1 < a2, then limx^Xx(t) = 0;
(ii) assume that a1 > a2, then limx^^x(t) =
From the third and sixth equations of system (1.5), we have limt ^xN(t) = b/p. Further, if I (t) = 0, we have the following limit system
dS = (1 -p)b + — - (p + v)S, t = kT,k e Z+, dt p
dN = b - pN' (2.5)
S(t+) = (1 - e)S(t), t = kT, N(t+) = N(t).
From the second and fourth equations of system (3.5), we have limt ^xN(t) = b/p and have the following limit systems of (3.5):
dS = (1 -p)b + p - (P + V)S, t/kT, dt p (2.6)
S(t+) = (1 - e)S(t), t = kT. According to Lemma 2.1, we know that the periodic solution of system (3.10) is of the form
Se(t) = (P(1 - p) +V)b + ( S* - (P(1 - p) + V)b)e-(P^v)(t-kT)f kT<t < (k + 1)t, (2.7) p(p + v) y p(p + v)
and it is globally asymptotically stable, where S* = (((1 - p)b + vb/p)(1 - 0)(e(p+v^T - 1)/((p +
v)(e(P+v)T - 1 + 0))).
Theorem 2.4. The infection-free periodic solution (Se(t), 0, b/i) of system (1.5) is globally attractive provided that R0 < 1, where
R = ((1 - p)b + vb/j)^^ - 1) 0 (d + y + a)(d + v)(e(i+v^T - 1 + 0)' ( . )
Proof. Since R0 < 1, we can choose e1 > 0 sufficiently small such that
/((1 - p)b + vb/u)(e(i+v)T - 1) \
¡¡( VV /' w (V-+ e!) <U + y + a. (2.9)
\ d + v)(e(d+v)T -1 + 0) 7 d '
It follows from the third equation of system (1.5) that
N(t) < b - ¡iN(t). (2.10)
There exists an integer k\ > 0 such that N(t) < b/i, t > k\T. From the first equation of system (1.5), we have
S(t) < (1 -p)b +--(i + v)S. (2.11)
For t > k\r, k > ki, we consider the following comparison differential system:
x (t) = (1 - ^b +--(i + v)x(t), t /kr,
1 (2.12)
x(t+) = (1 - 0)x(t), t = kr.
In view of Lemma 2.1, we know that the unique periodic solution of system (2.12) is of the form
- ((1 - p)b + vb/i)(e(l+v)T - 1)
S(t) <Se (t)+ ex < U J + e1 = 6, kr<t < (k + 1)t, k>k2/
(i + ^ (e(li+v)T - 1 + 0)
(2.13)
and it is globally asymptotically stable. From (1.5), we have
l(t) < p6I(t - t) - (i + y + a)I(t), t>kT, k > k2. (2.14)
From (2.9), we have that ¡56 < i + y + a. According to Corollary 2.3 we have
lim I (t)= 0. (2.15)
t —► 00
Therefore, for any e1 > 0 (sufficiently small), there exists an integer k3 > k2 such that I(t) < e for all t > k3T. From the third equation of system (1.5), we have
N(t) > b - fiN(t) - aei, t > k3T. (2.16)
Consider the comparison equation
z(t) = b - ae1 - fiz(t), for t > k3T. (2.17)
It is easy to see that limt^xz(t) = (b-ae1)/j. It follows by the comparison theorem that there exists an integer k4 > k3 such that
N(t) > b - ae1 - e1, V t> k4t. (2.18)
Since e1 is arbitrarily small, from limt^^ sup N(t) < b/j and (2.18) we have
lim N(t) = -. (2.19)
t ^^ jj
It follows from (2.15) and (2.19) that there exists k5 > k4 such that
I(t) < e1, N(t) > - - e1, for t > k5T. (2.20)
Hence, from the first equation of system (1.5) we have that
S(t) > (1 - p)b - jS(t) - pS(t)e1 + jj - se1 - S(t)^ b
= (1 - p)b + v--2e1v - (j + ¡5e1 + v)S(t), for t>k5T.
(2.21)
Consider the following comparison impulsive differential equations for t > k5T and k > k5,
u(t) = (1 - ^b + v— 2e1v - (j + ¡e1 + v)u(t), t = kT,
j (2.22)
u(t+) = (1 - Q)u(t), t = kT. In view of Lemma 2.1, we periodic solution of system
ue(t) = Y + (u* - r)e-(j+?e1+v)(t-kT), kT<t < (k + 1)t, (2.23)
which is globally asymptotically stable, where
T= (1 - p)b + v(b/p) - 2e\v u _r(l - 9)( 1 -
à + psi + v , u (1 - (1 - 0)e-(^+P£i+v)T) ' ( '
According to the comparison theorem for impulsive differential equation, there exists an integer k6 > k5 such that
S(t) >Ue (t) - si, kr<t < (k + 1)t, k>k6. (2.25)
Because s1 is arbitrarily small, it follows from (2.25) that
S () ((1 - p)b + v(b/^)(e(^)T - 1) 1)T 2
Se(t)_-^-, kT<t < (k + 1)T' (2'26)
is globally attractive, that is,
lim S(t)_ Se(t). (2' 27)
It follows from (2 ' 15), (2 . 19), (2. 27), and the restriction N(t) _ S(t) + I(t) + R(t) that limx^xR(t) _ b/p- Se(t) ' Hence, the infection-free periodic solution (Se(t), 0,b/¡) of system (1 '5) is globally attractive - The proof is completed - □
Corollary 2.5. The infection-free periodic solution (Se(t), 0, b/¡¡) of system (1 ' 5) is globally attractive, if 6 > do where do _ (e(p+v)T - 1)(((1 - p + v/p)/((p + y)(p + y + a))) - 1).
Theorem 2 ' 4 determines the global attractivity of (1 5) in Q for the case R0 < 1 Its realistic implication is that the infected computers vanish so the computer virus removed from the network ' Corollary 2 ' 5 implies that the computer virus will disappear if the vaccination rate is larger than 60 '
3. Permanence
In this section, we say the computer virus is local if the infectious population persists above a certain positive level for sufficiently large time . The locality viruses can be well captured and studied through the notion of permanence
Definition 3.1. System (1.5) is said to be uniformly persistent if there is an 0 (independent of the initial data) such that every solution (S(t), I(t), R(t), N(t)) with initial conditions (1.7) of system (1.5) satisfies
lim inf S(t) > y, lim inf I (t) > y, lim inf R(t) > y.
Definition 3.2. System (1.5) is said to be permanent if there exists a compact region Q0 e int Q such that every solution of system (1.5) with initial data (1.7) will eventually enter and remain in region Qo. Denote
0(1 - p)b(epT - 1)
Ri =, w V 1 m' (3.2)
(p + J + a) (epT - 1 + d)
Theorem 3.3. Suppose that Ri > 1. Then there is a positive constant mI such that each positive solution (S(t),I(t),R(t)) of system (1.5) satisfies I(t) > mI ,for t large enough.
Proof. Now, we will prove there exist mI > 0 and a sufficiently large tp such that I(t) > mI holds for all t > tp. Suppose that I(t) < mI for all t > t0. From the first equation of (1.5), we have
S(t) > (1 -p)b - (p + m)S■ (3.3)
Consider the following comparison system:
Z2(t) = (1 - p)b - (p + m)z2(t), t / kr,
Z2(t+) = (1 - d)z2(t), t = krS(t) > (1 - p)b - (p + m) S.
By Lemma 2.1, we know that, there exists t1 such that
(1 - p)b(e(p+m)T - 1) -
S(t) > Zz2(i) > m - e > P^mkwmT - 1+ 9) - e" S> a for '>'1. (3.5)
It follows from the second equation of (1.5) that I(t) > 0SI(t-t) - (p + y + a)I(t). Consider the comparison system Z3(t) = 0SI(t-t) - (p + y + a)z3(t). Noting that R1 > 1 and e is sufficiently small, we have 0S> (p + y + a).
Corollary 2.3 implies that t ^ ro, I(t) > z3(t) ^ This contradicts I(t) < b. Hence, we can claim that, for any t0 > 0, it is impossible that
I(t) <mI V t > to. (3.6)
By the claim, we are left to consider two cases. First, I(t) < mI for t large enough. Second, I(t) oscillates about mI for t large enough. Obviously, there is nothing to prove for the first case. For the second case, we can choose t2 > t1 and 0 satisfy
I (t2) = I (t2 + £) = m*p I (t) < mI, for t2 <t<t2 + £ (3.7)
I(t) is uniformly continuous since the positive solutions to (1.5) are ultimately bounded and I(t) is not effected by impulses.
Therefore, it is certain that there exists n (0 <n<T, and n is independent of the choice of t2) such that
I(t) > -y, for t2 < t < t2 + n■ (3-8)
In this case, we shall consider the following three possible cases in term of the sizes of n, & and t .
Case 1. If & < n<T, then it is obvious that I (t) > m\/2, for t e [t2,t2 + &].
Case 2. If n < & < t, then from the second equation of system (1.5), we obtain I(t) > -(p + y + a)I(t). Since I(t2) = mI, it is obvious that I(t) > m}e-(Y+a+p)T=mf, for t e [t2,t2 + &].
Case 3. If n < t < &, it is easy to obtain that I(t) > m*j* for t e [t2,t2 + t]. Then, proceeding exactly the proof for above claim, we have that I(t) > m\* for t e [t2 + T,t2 + &].
Owing to the randomicity of t2, we obtain that there exists mI = min{m*I/2,m*I*}, such that I(t) > mI holds for all t > tp. The proof of Theorem 3.3 is completed. □
Theorem 3.4. Suppose R1 > 1. Then system (1.5) is permanent.
Proof. Let (S(t), I(t), N(t)) be any solution to system (1.5). First, from the first equation of system (1.5), we have S(t) > (1 - p)b - (p + f}b)S. Consider the following comparison system:
z1(t)= (1 - p)b - U + pfyz^t), tf kr,
zi(t+) = (1 - d)zi(t), t = kr.
By Lemmas 2.1 and 2.2, we know that for any sufficiently small e> 0, there exists ti (ti is sufficiently large) such that
(1 - p)b(e(^mï- 1)
sm > z2 m > m - * > ^ - J f>- £ f ms> kT<t £ (k+1)T.
(3.10)
From the third equation of (1.5), we have N(t) > n-pN(t)-aN(t). It is easy to see that N(t) > b/(n + a) - e f mN. Let Q,o = {(S,I,N) e R3+ | mi < S, mi < I, mN < N < b/p, S + 1 < b/^j. By Theorem 3.3 and above discussions, we know that the set Q.0 is a global attractor in Q., and of course, every solution of system (1.5) with initial conditions (1.7) will eventually enter and remain in region Q0. Therefore, system (1.5) is permanent. The proof is completed. □
Corollary 3.5. It follows from Theorem 3.4 that the system (1.5) is uniformly persistent provided that 6 < 61, where 61 = (epT - 1)((5(1 - p)b/((p + a)(p + a + y))) - 1).
4. Numerical Simulations
In this section, we perform some numerical simulations to show the geometric impression of our results. To demonstrate the global attractivity of infection-free periodic solution to system (1.5), we take the following parameter values: b = 1, p = 0.5, j = 0.3, a = 0.02, ¡5 = 0.3, v = 0.7, 6 = 0.4, and t = 1. In this case, we have R0 = 0.5894 < 1. In Figures 3(a), 3(b), and 3(c) display respectively the susceptible, infected and recovered population of system (1.5) with initial conditions: S(0) = 3, I(0) = 4 and R(0) = 5. Figure 3(d) shows their corresponding phase-portrait.
To demonstrate the permanence of system (1.5) we take following set parameter values: b = 10, p = 0.3, y = 0.3, a = 0.02, ¡5 = 0.3, 6 = 0.2, v = 0.7, and t = 1. In this case, we have Ri = 2.2195 > 1. In Figures 4(a), 4(b), and 4(c) display, respectively, the susceptible, infected and recovered population of system (1.5) with initial conditions: S(0) = 3, I(0) = 4, and R(0) = 5. Figure 4(d) shows their corresponding phase portrait.
4.5 |-
¡5 2.5
■a 12
S 13 8 12
T3 2 11
■■a 7
14 12 10 8 6
Figure 4: permanence of system.
5. Conclusion
We have analyzed the delayed SIRS model with pulse vaccination and varying total population size. We have shown that R1 > 1 or d < d1 implies that the disease will be endemic, whereas R0 < 1 or d > d0 implies that the disease will fade out. We have also established sufficient condition for the permanence of the model. Our results indicate that a short interpulse time or a large pulse vaccination rate will lead to eradication of the computer virus.
In this paper, we have only discussed two cases: (i) R0 < 1 (or d > d0) and (ii) R1 > 1 (or d < 01). But for closed interval [R0, R1] (or [0i,00]), the dynamical behavior of model (3) have not been studied, and the threshold parameter for the reproducing number (or the pulse vaccination rate) between the extinction of the computer viruses and the uniform persistence of the viruses have not been obtained. These issues would be left to our future consideration.
Acknowledgment
This paper is supported by the National Natural Science Foundation of China (no. 61170320), the Natural Science Foundation of Guangdong Province (no. S2011040002981) and the Scientific research Foundation of Guangdong Medical College (no. KY1048).
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